Avro vs Parquet

Let’s talk about the difference between Avro and Parquet.

Avro and Parquet are popular file formats in the Hadoop ecosystem for storing and processing data. However, they have some key differences that make them suitable for different use cases. Here’s a comparison between the two formats:

Avro

Avro is an open-source data format for serialising and exchanging data. Although it’s part of the Hadoop project, it can be used in any application without Hadoop libraries.

• Format: Avro is a row-based (row-oriented) storage format. It stores data in rows, making it suitable for write-heavy workloads and fast data serialization.
• Schema evolution: Avro supports schema evolution, which means you can modify the schema without needing to rewrite existing data. The schema is stored with the data, making it self-describing.
• Compression: Avro provides good compression, but it’s generally not as efficient as Parquet in terms of storage space.
  Avro supports multiple compression codecs such as Deflate, Snappy and Bzip2.

import avro.schema
from avro.datafile import DataFileReader, DataFileWriter
from avro.io import DatumReader, DatumWriter
import snappy

# Load the Avro schema
schema = avro.schema.Parse(open("example.avsc", "rb").read())

# Open the input Avro file
reader = DataFileReader(open("input.avro", "rb"), DatumReader())

# Open the output Avro file with Snappy compression
writer = DataFileWriter(open("output.avro", "wb"), DatumWriter()…